{ "data": { "a": { "slug": "glm-4-5-air", "name": "GLM-4.5-Air", "vendor": "Z.ai (Zhipu AI)", "vendorUrl": "https://z.ai", "kind": "weights", "category": "open-weights", "summary": "GLM-4.5-Air is Z.ai's open-weight mixture-of-experts model for English and Chinese, 106B total and 12B active parameters, with thinking that turns off per request, tool calling and a 128K context, published in BF16 and FP8 under MIT.", "url": "https://www.anchorterminal.com/tools/glm-4-5-air", "markdownUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json", "repo": "https://huggingface.co/zai-org/GLM-4.5-Air", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "zai-org/GLM-4.5-Air" }, { "registry": "huggingface", "name": "zai-org/GLM-4.5-Air-FP8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named on the card. Running it yourself costs the hardware, 221.0 GB for the BF16 files and 112.6 GB for Z.ai's FP8 build. Z.ai's own API also serves GLM-4.5-Air (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://github.com/zai-org/GLM-4.5", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "chinese", "mit", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2025-08-11", "graded": true, "anchor": { "graded": true, "score": 59.1, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 51, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 50, "interface": 85, "reach": 65, "risk": 65, "upkeep": 38 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.", "bestFor": "Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.", "strengths": [ "Released support in llama.cpp, vLLM, SGLang and Transformers", "Plain MIT with no gate, no account and no use policy", "Safetensors only, with a checksum on every shard in the publisher's own organisation", "Z.ai documents the glm45 tool and reasoning parsers, and thinking turns off per request", "Hosted by Z.ai and Novita for agents without the hardware" ], "weaknesses": [ "Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own", "No limitations, architecture, context or knowledge cut-off on the card, and no training cut-off anywhere", "No agent guidance or security contact", "No reply from Z.ai in its discussions since 30 July 2025", "Structured output isn't documented for the weights" ], "agentNotes": [ "Z.ai's FP8 recipe is `python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45`, and 2 H100s are enough below the full 128K context", "Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7", "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, which makes the template add /nothink and an empty think block", "Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON", "Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.1 } ], "editorialScores": { "capability": 50, "interface": 85, "reach": 65, "risk": 65, "upkeep": 38 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# per Z.ai's FP8 card (4 H100 for the full 128K context):\npython3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 \\\n --tool-call-parser glm45 --reasoning-parser glm45 --served-model-name glm-4.5-air --port 8000", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"glm-4.5-air\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"chat_template_kwargs\": {\"enable_thinking\": false}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/glm-4-5-air" }, "sameCompany": [ "glm-4-7-flash", "glm-5-3", "glm-5-3-flash" ], "weights": { "trainedBy": "Z.ai", "params": "106B", "active": "12B", "context": 131072, "goodFor": "Mid-size mixture-of-experts reasoning model for English and Chinese agent, coding and tool-calling work, with thinking that turns off per request.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "GLM-4.5-Air-FP8", "url": "https://huggingface.co/zai-org/GLM-4.5-Air-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 112.6, "memoryGB": 192, "format": "safetensors", "note": "112.6 GB × 1.2 = 135.1 GB, more than the reference machine's 107.4 GB GPU share" }, { "name": "GLM-4.5-Air", "url": "https://huggingface.co/zai-org/GLM-4.5-Air", "who": "publisher", "precision": "bf16", "sizeGB": 221, "memoryGB": 512, "format": "safetensors" } ], "run": "python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45 --served-model-name glm-4.5-air --port 8000", "runBuild": "GLM-4.5-Air-FP8", "checked": "2026-10-21", "minMemoryGB": 192 }, "family": { "lab": "Z.ai (Zhipu AI)", "family": "GLM-4", "url": "https://www.anchorterminal.com/open-weights/#zai", "others": [ { "name": "GLM-4-9B", "url": "https://huggingface.co/zai-org/glm-4-9b", "params": "9B" }, { "name": "GLM-4-9B-Chat", "url": "https://huggingface.co/zai-org/glm-4-9b-chat", "params": "9B" }, { "name": "GLM-4-9B-Chat-1M", "url": "https://huggingface.co/zai-org/glm-4-9b-chat-1m", "params": "9B" }, { "name": "GLM-4V-9B", "url": "https://huggingface.co/zai-org/glm-4v-9b", "params": "9B" }, { "name": "GLM-4-9B-0414", "url": "https://huggingface.co/zai-org/GLM-4-9B-0414", "params": "9B" }, { "name": "GLM-4-32B-Base-0414", "url": "https://huggingface.co/zai-org/GLM-4-32B-Base-0414", "params": "32B" }, { "name": "GLM-4-32B-0414", "url": "https://huggingface.co/zai-org/GLM-4-32B-0414", "params": "32B" }, { "name": "GLM-Z1-9B-0414", "url": "https://huggingface.co/zai-org/GLM-Z1-9B-0414", "params": "9B" }, { "name": "GLM-Z1-32B-0414", "url": "https://huggingface.co/zai-org/GLM-Z1-32B-0414", "params": "32B" }, { "name": "GLM-Z1-Rumination-32B-0414", "url": "https://huggingface.co/zai-org/GLM-Z1-Rumination-32B-0414", "params": "32B" }, { "name": "GLM-4.1V-9B-Base", "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Base", "params": "9B" }, { "name": "GLM-4.1V-9B-Thinking", "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking", "params": "9B" }, { "name": "GLM-4.5-Base", "url": "https://huggingface.co/zai-org/GLM-4.5-Base", "params": "355B-A32B" }, { "name": "GLM-4.5", "url": "https://huggingface.co/zai-org/GLM-4.5", "params": "355B-A32B" }, { "name": "GLM-4.5-Air-Base", "url": "https://huggingface.co/zai-org/GLM-4.5-Air-Base", "params": "106B-A12B" }, { "name": "GLM-4.5V", "url": "https://huggingface.co/zai-org/GLM-4.5V", "params": "106B-A12B" }, { "name": "GLM-4.6", "url": "https://huggingface.co/zai-org/GLM-4.6", "params": "355B-A32B" }, { "name": "GLM-4.6V", "url": "https://huggingface.co/zai-org/GLM-4.6V", "params": "106B" }, { "name": "GLM-4.6V-Flash", "url": "https://huggingface.co/zai-org/GLM-4.6V-Flash", "params": "9B" }, { "name": "GLM-4.7", "url": "https://huggingface.co/zai-org/GLM-4.7", "params": "355B-A32B" }, { "name": "GLM-4.7-Flash", "url": "https://huggingface.co/zai-org/GLM-4.7-Flash", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/glm-4-7-flash" } ], "olderCount": 19, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "JINGSHENG HENGXING TECHNOLOGY PTE.LTD (operator of Z.ai per its terms of use, and registrant of z.ai, per the first batch's check on 21 October 2026)", "domain": "z.ai", "domainRegistered": "2017-12-15", "domainNote": "z.ai was registered in 2017 and transferred in October 2024, so its age says little about the company's.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/zai-org/GLM-4.5-Air/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "Z.ai's GitHub README links the zai-org organisation on Hugging Face for every GLM-4.5 checkpoint. Hugging Face hasn't verified the organisation.", "Nothing is hosted for this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, 10 commits from 20 July to 11 August 2025, the upload and card updates.", "z.ai/.well-known/security.txt, docs.z.ai/.well-known/security.txt and www.zhipuai.cn/.well-known/security.txt answered 404 on 21 October 2026, and bigmodel.cn answers that path with its web app." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.json" }, "answer": "GLM-4.5-Air scores 59.1 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 5 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep.", "b": { "slug": "ling-3-0-tiny", "name": "Ling-3.0-tiny", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://www.inclusion-ai.org", "kind": "weights", "category": "open-weights", "summary": "Ling-3.0-tiny is inclusionAI's open-weight hybrid reasoning mixture of experts with 7.9B parameters and 1.3B active, mixing Kimi Delta Attention and MLA layers 3 to 1, with switchable thinking and a 131,072-token context, in BF16, FP8, INT4 and GGUF under MIT.", "url": "https://www.anchorterminal.com/tools/ling-3-0-tiny", "markdownUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json", "repo": "https://huggingface.co/inclusionAI/Ling-3.0-tiny", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/Ling-3.0-tiny" }, { "registry": "huggingface", "name": "inclusionAI/Ling-3.0-tiny-fp8" }, { "registry": "huggingface", "name": "inclusionAI/Ling-3.0-tiny-int4" }, { "registry": "huggingface", "name": "inclusionAI/Ling-3.0-tiny-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under MIT (named in the card's metadata; the repositories hold no LICENSE file). Running it yourself costs the hardware, 15.8 GB for the BF16 files, 8.4 GB for the FP8 build, 5.8 GB for INT4 and 4.8 GB for the Q4_K_M GGUF (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/inclusionAI/Ling-3.0-tiny", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "hybrid-attention", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "int4", "gguf", "self-hosted" ], "lastRelease": "2026-08-31", "graded": true, "anchor": { "graded": true, "score": 55, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 65, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 31, "interface": 60, "reach": 90, "risk": 50, "upkeep": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.", "bestFor": "Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT", "strengths": [ "About 6 GB of memory for inclusionAI's own Q4_K_M GGUF, with FP8, INT4 and three more GGUF precisions", "Runs in released Transformers, vLLM, SGLang and llama.cpp", "MIT with no gate, no account and no use policy, with checksummed files in inclusionAI's own organisation", "Thinking switchable per request, several tool calls a turn and 131,072 tokens of native context", "A training-content summary with sources, size and an October 2025 cut-off, and a guardrail probe made for this model" ], "weaknesses": [ "No hosted provider today, and Ollama support is only an unmerged pull request", "The card's SGLang recipe asks for built-in MTP the files don't have, and the safetensors index misstates the total size", "The documented vLLM route needs --trust-remote-code", "No safety results for the model, no structured output and no security contact", "Card and SGLang disagree on which tool and reasoning parsers to use, and most discussions since August have no reply" ], "agentNotes": [ "Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3", "With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code", "With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer", "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks", "Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20", "The template keeps earlier reasoning in the prompt, which grows long agent sessions", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented", "For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55 } ], "editorialScores": { "capability": 31, "interface": 60, "reach": 90, "risk": 50, "upkeep": 56 }, "provenanceScore": 57, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768\n# or with vLLM 0.32.0, per the card's serve command:\nvllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align \\\n --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"inclusionAI/Ling-3.0-tiny\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}, \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 20}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ling-3-0-tiny" }, "sameCompany": [ "ming-3-0-asr-tiny", "ming-3-0-audio-flash", "ming-3-0-audio-tiny", "ming-3-0-tts-tiny" ], "weights": { "trainedBy": "inclusionAI (Ant Group)", "params": "7.9B (1.3B active)", "context": 131072, "goodFor": "Small, fast hybrid reasoning model for local agents, tool calling and coding on a laptop, with thinking switchable per request.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ling-3.0-tiny-int4", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-int4", "who": "publisher", "precision": "int4", "sizeGB": 5.8, "memoryGB": 8, "format": "safetensors", "note": "compressed-tensors, 4-bit weights in groups of 32, with attention and dense layers kept at higher precision" }, { "name": "Ling-3.0-tiny-Q6_K", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 6.5, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Ling-3.0-tiny-Q5_K_M", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 5.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Ling-3.0-tiny-Q4_K_M", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 4.8, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Ling-3.0-tiny-fp8", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-fp8", "who": "publisher", "precision": "fp8", "sizeGB": 8.4, "memoryGB": 12, "format": "safetensors" }, { "name": "Ling-3.0-tiny-Q8_0", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 8.4, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Ling-3.0-tiny", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny", "who": "publisher", "precision": "bf16", "sizeGB": 15.8, "memoryGB": 24, "format": "safetensors" } ], "run": "llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768", "runBuild": "Ling-3.0-tiny-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "Ling 3", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "Ling-3.0-flash", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base-30T", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-30T", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base-midtrain", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-midtrain", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-Fin", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-Fin", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-VL", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-VL", "params": "124B total, 5.5B active" }, { "name": "Ling-3.0-flash-dspark", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-dspark", "params": "1.36B (draft)" }, { "name": "Ling-3.0-flash-singprobe", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-singprobe", "params": "5.18M (probe)" }, { "name": "Ling-3.0-tiny-base", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-base-30T", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-30T", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-base-midtrain", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-midtrain", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-singprobe", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-singprobe", "params": "3.22M (probe)" }, { "name": "Ling-3.1-flash", "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash", "params": "560B total, 25B active" } ], "olderCount": 54, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Ant Intelligence (Hang Zhou) Technology Co., Ltd (named as provider in the training-content summary); inclusionAI, Ant Group in the site footer of inclusion-ai.org", "domain": "inclusion-ai.org", "domainRegistered": "2025-04-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/Ling-3.0-tiny/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI. 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yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air.json", "title": "Agents-A1-4B vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.json", "title": "Agents-A1-4B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.json", "title": "Apertus v1.5 8B vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny.json", "title": "Apertus v1.5 8B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air.json", "title": "Apodex-1.1-mini vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-0-tiny.json", "title": "Apodex-1.1-mini vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air.json", "title": "BigBang-v1 vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny.json", "title": "BigBang-v1 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air.json", "title": "Cosmos-Reason2-2B vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny.json", "title": "Cosmos-Reason2-2B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-5-air.json", "title": "Darwin-36B-Opus vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-0-tiny.json", "title": "Darwin-36B-Opus vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air.json", "title": "DeepSeek-V3.2 vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny.json", "title": "DeepSeek-V3.2 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air.json", "title": "DeepSeek V4.1 Flash vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.json", "title": "DeepSeek V4.1 Flash vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-5-air.json", "title": "DeepSeek-V4-Flash-0731 vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ling-3-0-tiny.json", "title": "DeepSeek-V4-Flash-0731 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-5-air.json", "title": "DiffusionGemma 26B A4B IT vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-0-tiny.json", "title": "DiffusionGemma 26B A4B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-5-air.json", "title": "Gemma 4 12B IT vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny.json", "title": "Gemma 4 12B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air.json", "title": "Gemma 4 26B A4B IT vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny.json", "title": "Gemma 4 26B A4B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air.json", "title": "Gemma 4 31B IT vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny.json", "title": "Gemma 4 31B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-5-air.json", "title": "Gemma 4 E2B IT vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ling-3-0-tiny.json", "title": "Gemma 4 E2B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-4-5-air.json", "title": "Gemma 4 E4B IT vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.json", "title": "Gemma 4 E4B IT vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-4-7-flash.json", "title": "GLM-4.5-Air vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3.json", "title": "GLM-4.5-Air vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.json", "title": "GLM-4.5-Air vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-120b.json", "title": "GLM-4.5-Air vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-20b.json", "title": "GLM-4.5-Air vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-granite-4-2-3b.json", "title": "GLM-4.5-Air vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy3.json", "title": "GLM-4.5-Air vs Hy3", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling.json", "title": "GLM-4.5-Air vs Inkling", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling-small.json", "title": "GLM-4.5-Air vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "GLM-4.5-Air vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3.json", "title": "GLM-4.5-Air vs Kimi K3", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kolibri-1.json", "title": "GLM-4.5-Air vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-m-1.json", "title": "GLM-4.5-Air vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1.json", "title": "GLM-4.5-Air vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-xs-2-1.json", "title": "GLM-4.5-Air vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.json", "title": "GLM-4.5-Air vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-distill-qwen-9b.json", "title": "GLM-4.5-Air vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5.json", "title": "GLM-4.5-Air vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm5-2b.json", "title": "GLM-4.5-Air vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5.json", "title": "GLM-4.5-Air vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-7.json", "title": "GLM-4.5-Air vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-muse-glimmer-30b.json", "title": "GLM-4.5-Air vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "GLM-4.5-Air vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-30b-a3b.json", "title": "GLM-4.5-Air vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-4b.json", "title": "GLM-4.5-Air vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "GLM-4.5-Air vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-super-120b-a12b.json", "title": "GLM-4.5-Air vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-ultra-550b-a55b.json", "title": "GLM-4.5-Air vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-occamy-1-0.json", "title": 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{ "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-ultra-550b-a55b.json", "title": "Ling-3.0-tiny vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-occamy-1-0.json", "title": "Ling-3.0-tiny vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-olmo-3-7b-think.json", "title": "Ling-3.0-tiny vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-35b-a3b.json", "title": "Ling-3.0-tiny vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-397b.json", "title": "Ling-3.0-tiny vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-9b.json", "title": "Ling-3.0-tiny vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Ling-3.0-tiny vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-4b-instruct-2507.json", "title": "Ling-3.0-tiny vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b.json", "title": "Ling-3.0-tiny vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-122b-a10b.json", "title": "Ling-3.0-tiny vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-2b.json", "title": "Ling-3.0-tiny vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-397b-a17b.json", "title": "Ling-3.0-tiny vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-4b.json", "title": "Ling-3.0-tiny vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b.json", "title": "Ling-3.0-tiny vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-27b.json", "title": "Ling-3.0-tiny vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b.json", "title": "Ling-3.0-tiny vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-27b.json", "title": "Ling-3.0-tiny vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-flash-next.json", "title": "Ling-3.0-tiny vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-next.json", "title": "Ling-3.0-tiny vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-2b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-32b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-4b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-8b-instruct.json", "title": "Ling-3.0-tiny vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwopus3-8-27b-flash-v2.json", "title": "Ling-3.0-tiny vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-smollm3-3b.json", "title": "Ling-3.0-tiny vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-spark-x2-6-4b.json", "title": "Ling-3.0-tiny vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-27b.json", "title": "Ling-3.0-tiny vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ling-3.0-tiny vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-thinkingcap-qwen3-8-27b.json", "title": "Ling-3.0-tiny vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 19, "edge": "glm-4-5-air", "glm-4-5-air": 50, "key": "capability", "ling-3-0-tiny": 31, "name": "Capability", "weight": 35 }, { "by": 25, "edge": "ling-3-0-tiny", "glm-4-5-air": 65, "key": "reach", "ling-3-0-tiny": 90, "name": "Reach", "weight": 25 }, { "by": 15, "edge": "glm-4-5-air", "glm-4-5-air": 65, "key": "risk", "ling-3-0-tiny": 50, "name": "Risk", "weight": 20 }, { "by": 25, "edge": "glm-4-5-air", "glm-4-5-air": 85, "key": "interface", "ling-3-0-tiny": 60, "name": "Agent interface", "weight": 10 }, { "by": 18, "edge": "ling-3-0-tiny", "glm-4-5-air": 38, "key": "upkeep", "ling-3-0-tiny": 56, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "GLM-4.5-Air scores 59.1 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 5 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "glm-4-5-air": "An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.", "ling-3-0-tiny": "A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny", "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.min.md" }, "markdown": "GLM-4.5-Air scores 59.1 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 5 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep. Both do open-weight models you run yourself.\n\n- GLM-4.5-Air: grade C, 59.1/100, rank #62 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-5-air.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json\n- Ling-3.0-tiny: grade C, 55/100, rank #81 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### GLM-4.5-Air (C)\n\nGood for: Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.\n\nAhead on:\n- Capability, 50 against 31\n- Risk, 65 against 50\n- Agent interface, 85 against 60\n\nWatch for: Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own\n\n### Ling-3.0-tiny (C)\n\nGood for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT\n\nAhead on:\n- Reach, 90 against 65\n- Documentation and upkeep, 56 against 38\n\nWatch for: No hosted provider today, and Ollama support is only an unmerged pull request\n\n\n## Score by category\n\n| Category | Weight | GLM-4.5-Air | Ling-3.0-tiny | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 50 | 31 | GLM-4.5-Air +19 |\n| Reach | 25% (25 this run) | 65 | 90 | Ling-3.0-tiny +25 |\n| Risk | 20% (20 this run) | 65 | 50 | GLM-4.5-Air +15 |\n| Agent interface | 10% (10 this run) | 85 | 60 | GLM-4.5-Air +25 |\n| Documentation and upkeep | 10% (10 this run) | 38 | 56 | Ling-3.0-tiny +18 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.1 · C** | **55 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-4.5-Air | Ling-3.0-tiny |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | inclusionAI (Ant Group) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-08-11 | 2026-08-31 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**GLM-4.5-Air.** An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.\n\n**Ling-3.0-tiny.** A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.\n\n## Before you call either\n\n### GLM-4.5-Air\n\n1. Z.ai's FP8 recipe is `python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45`, and 2 H100s are enough below the full 128K context\n2. Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7\n3. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, which makes the template add /nothink and an empty think block\n4. Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON\n5. Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072\n6. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights\n\n### Ling-3.0-tiny\n\n1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3\n2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code\n3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer\n4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks\n5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20\n6. The template keeps earlier reasoning in the prompt, which grows long agent sessions\n7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches\n\n## Questions\n\n### Which is better for AI agents, GLM-4.5-Air or Ling-3.0-tiny?\n\nGLM-4.5-Air scores 59.1 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 5 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-5-air\", \"b\": \"ling-3-0-tiny\"}`. From a terminal: `anchor compare glm-4-5-air ling-3-0-tiny`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json and https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json\n\n## Other comparisons with GLM-4.5-Air or Ling-3.0-tiny\n\n- [Agents-A1-4B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air.md)\n- [Agents-A1-4B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.md)\n- [Apertus v1.5 8B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.md)\n- [Apertus v1.5 8B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny.md)\n- [Apodex-1.1-mini vs GLM-4.5-Air](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air.md)\n- [Apodex-1.1-mini vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-0-tiny.md)\n- [BigBang-v1 vs GLM-4.5-Air](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air.md)\n- [BigBang-v1 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny.md)\n- [Cosmos-Reason2-2B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air.md)\n- [Cosmos-Reason2-2B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny.md)\n- [Darwin-36B-Opus vs GLM-4.5-Air](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-5-air.md)\n- [Darwin-36B-Opus vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-0-tiny.md)\n- [DeepSeek-V3.2 vs GLM-4.5-Air](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air.md)\n- [DeepSeek-V3.2 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny.md)\n- [DeepSeek V4.1 Flash vs GLM-4.5-Air](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air.md)\n- [DeepSeek V4.1 Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.md)\n- [DeepSeek-V4-Flash-0731 vs GLM-4.5-Air](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-5-air.md)\n- [DeepSeek-V4-Flash-0731 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ling-3-0-tiny.md)\n- [DiffusionGemma 26B A4B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-5-air.md)\n- [DiffusionGemma 26B A4B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 12B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-5-air.md)\n- [Gemma 4 12B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 26B A4B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air.md)\n- [Gemma 4 26B A4B IT vs 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